<html><head><title>Summarizes a GAMLSS fitted model</title>
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<link rel="stylesheet" type="text/css" href="Rchm.css">
</head>
<body>

<table width="100%"><tr><td>summary.gamlss(gamlss)</td><td align="right">R Documentation</td></tr></table><object type="application/x-oleobject" classid="clsid:1e2a7bd0-dab9-11d0-b93a-00c04fc99f9e">
<param name="keyword" value="R:   summary.gamlss">
<param name="keyword" value=" Summarizes a GAMLSS fitted model">
</object>


<h2>Summarizes a GAMLSS fitted model</h2>


<h3>Description</h3>

<p>
<code>summary.gamlss</code> is the GAMLSS specific method for the generic function <code>summary</code> which summarize 
objects returned by modelling functions.
</p>


<h3>Usage</h3>

<pre>
## S3 method for class 'gamlss':
summary(object, type = c("vcov", "qr"), ...)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>object</code></td>
<td>
a GAMLSS fitted model</td></tr>
<tr valign="top"><td><code>type</code></td>
<td>
the default value <code>vcov</code> uses the <code>vcov()</code> method for gamlss to get the 
variance-covariance  matrix of the estimated beta coefficients, see details below. 
The alternative <code>qr</code> is the original method used in gamlss to 
estimated the standard errors but it is not reliable since it do not take into the account the inter-correlation between
the distributional parameters <code>mu</code>, <code>sigma</code>, <code>nu</code> and <code>tau</code>.
</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
for extra arguments</td></tr>
</table>

<h3>Details</h3>

<p>
Using the  default value <code>type="vcov"</code>, the <code>vcov()</code> method for gamlss is used to get  
the variance covariance matrix (and consequently the standard errors)of the beta parameters. 
The variance covariance matrix is  calculated using the inverse of the numerical second derivatives
of the observed information matrix. This is a more reliable method since it take into the account the 
inter-correlation between the all the parameters. The <code>type="qr"</code> assumes that the parameters are fixed 
at the estimated values. Note that both methods are not appropriate and should be used with caution if smoothing 
terms are used in the fitting.
</p>


<h3>Value</h3>

<p>
Print summary of a GAMLSS object</p>

<h3>Note</h3>




<h3>Author(s)</h3>

<p>
Mikis Stasinopoulos <a href="mailto:d.stasinopoulos@londonmet.ac.uk">d.stasinopoulos@londonmet.ac.uk</a>, Bob Rigby <a href="mailto:r.rigby@londonmet.ac.uk">r.rigby@londonmet.ac.uk</a> and Calliope Akantziliotou
</p>


<h3>References</h3>

<p>
Rigby, R. A. and  Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), 
<EM>Appl. Statist.</EM>, <B>54</B>, part 3, pp 507-554.
</p>
<p>
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2003) Instructions on how to use the GAMLSS package in R.
Accompanying documentation in the current GAMLSS  help files, (see also  <a href="http://www.gamlss.com/">http://www.gamlss.com/</a>).
</p>
<p>
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
<EM>Journal of Statistical Software</EM>, Vol. <B>23</B>, Issue 7, Dec 2007, <a href="http://www.jstatsoft.org/v23/i07">http://www.jstatsoft.org/v23/i07</a>.
</p>


<h3>See Also</h3>

<p>
<code><a href="gamlss.html">gamlss</a></code>, <code><a href="deviance.gamlss.html">deviance.gamlss</a></code>,  <code><a href="fitted.gamlss.html">fitted.gamlss</a></code>
</p>


<h3>Examples</h3>

<pre>
data(aids)
h&lt;-gamlss(y~poly(x,3)+qrt, family=PO, data=aids) # 
summary(h)
rm(h)
</pre>



<hr><div align="center">[Package <em>gamlss</em> version 1.7-9 <a href="00Index.html">Index]</a></div>

</body></html>
